{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201906</td>\n",
       "      <td>12990</td>\n",
       "      <td>12000</td>\n",
       "      <td>98669</td>\n",
       "      <td>323172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>201907</td>\n",
       "      <td>13209</td>\n",
       "      <td>12000</td>\n",
       "      <td>94918</td>\n",
       "      <td>308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>201908</td>\n",
       "      <td>13388</td>\n",
       "      <td>12500</td>\n",
       "      <td>94569</td>\n",
       "      <td>303830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>201909</td>\n",
       "      <td>13421</td>\n",
       "      <td>12500</td>\n",
       "      <td>90804</td>\n",
       "      <td>292998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>201910</td>\n",
       "      <td>13337</td>\n",
       "      <td>12500</td>\n",
       "      <td>87620</td>\n",
       "      <td>284579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>201911</td>\n",
       "      <td>13451</td>\n",
       "      <td>12500</td>\n",
       "      <td>84685</td>\n",
       "      <td>277672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0  201906        12990          12000     98669     323172\n",
       "1  201907        13209          12000     94918     308802\n",
       "2  201908        13388          12500     94569     303830\n",
       "3  201909        13421          12500     90804     292998\n",
       "4  201910        13337          12500     87620     284579\n",
       "5  201911        13451          12500     84685     277672"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15393706933077628, 0.12315188546707401, 0.10815511897063086, 0.09083914992306491, 0.05406640947305042, 0.46985036683540354]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照只能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "区块链工程师平均工资为20701\n"
     ]
    }
   ],
   "source": [
    "sql=f'select AVG(monthly_salary)  from _{year}{month} where expert_blockchain=1 and monthly_salary>0'\n",
    "salary_blockchain=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'区块链工程师 平均工资为{salary_blockchain}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "算法工程师平均工资为19645\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and career='算法工程师'\"\n",
    "salary_algorithm=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'算法工程师 平均工资为{salary_algorithm}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系统架构师平均工资为23031\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and career='系统架构师'\"\n",
    "salary_architect=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'系统架构师 平均工资为{salary_architect}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Java工程师平均工资为14080\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and pl_java=1\"\n",
    "salary_java=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Java工程师 平均工资为{salary_java}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python工程师平均工资为17882\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month} where monthly_salary>0 and pl_python=1\"\n",
    "salary_python=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Python工程师 平均工资为{salary_python}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func['职能']=['区块链工程师', '算法工程师','系统架构师', 'Java工程师',  'Python工程师']\n",
    "data_func['工资']=[salary_blockchain, salary_algorithm, salary_architect, salary_java, salary_python]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职能</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>系统架构师</td>\n",
       "      <td>23031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>区块链工程师</td>\n",
       "      <td>20701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>19645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Python工程师</td>\n",
       "      <td>17882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Java工程师</td>\n",
       "      <td>14080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          职能     工资\n",
       "2      系统架构师  23031\n",
       "0     区块链工程师  20701\n",
       "1      算法工程师  19645\n",
       "4  Python工程师  17882\n",
       "3    Java工程师  14080"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_func.sort_values(by='工资', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
